Pang and Lee (2005) have studied prediction of product ratings, which may be particularly relevant due to the correlation we find between product rating and the helpfulness of the review (discussed in Section 5).

in opinion and sentiment analysis, the focus is on distinguishing between statements of fact vs. opinion, and on detecting the polarity of sentiments being expressed.


Structural features are observations of the document structure and formatting.

more recent work on one of the major systems, e-rater 2.0, has focused on systematizing and simplifying the set of features used (Attali and Burstein 2006).


In CF, an active user (implicitly) specifies items that he likes.

these ranked top-N articles were then compared to the articles in the test data that were edited by the same user to measure the precisions for the user.


Recommender systems (Resnick and Varian, 1997) help users select particular items (e.g, movies, books, music, and TV programs) that match their taste from a large number of choices by providing recommendations.

the error bars represent standard deviations.


we found in preliminary experiments that smoothing query probabilities hurt performance in our application.


We then generalize that model to an approximated Polya distribution model that is better suited to CF and CBF.

the differences in precision were small.


Because of the size of the smallest corpora meant that a high cutoff would remove to many terms for a fair test, a cutoff of 5 was applied.

mILOG ± is more accurate than TF-IDF † for both measures of accuracy now, indicating it is a better weight function for very large data sets.


In fact, if both of these conditions are fulfilled, it is very likely that the optimal model will have some weights with infinite values.

we divide our main features into two types according to whether they are most naturally used in a CRF or a semi-CRF.


We have shown that order-1 semi-Markov conditional random fields are strictly more expressive than order-1 Markov CRFs, and that the added expressivity enables the use of features that lead to improvements on a segmentation task.

adding semi-CRF indicator features results in a 38% error reduction without CRF-type features, and 18% with them.


As mentioned in Section 3, for the word based approach the implicit feature space representation can have considerably higher dimensionality and be sparser than for the character based approach.

section 4 provides an empirical performance comparison of the abovementioned approaches, while in section 5 the author unmasking method is appraised.


By default w |q| = 0, modified by one of the following functions: specific length: where τ indicates a user defined maximum sequence length.

longer texts would be required to adequately populate the feature space.


We conducted two groups of experiments, the first with simple syntactic tuple matching, and the second with semantic tuple clustering as described in section 4.3 We compare our results to a state-of-the-art supervised system similar to the system described in (Kambhatla, 2004).

the proposed approach achieves superior performance which could be compared to supervised techniques with reasonable training data.


Our experience with the incremental sample selection provides the following hints.

we evaluate the impact of training data size on the NER performance across domains.


when applied to the 'Rondane' test set, this in-  Table 7: Exact match error counts for three models, viz.


In this study, ExRetriever has been evaluated using the BNC, WN as a knowledge base and TFIDF (as shown in formula 1) (Agirre and de la Calle, 2004) 5 .

it also seems that the knowledge from spBNC always degrades the performance of their combinations 9 .


• Monosemous W (queryW): (OR monosemous-words).

for TSWEB and TSBNC the better results have been obtained using occurrences (the weights are only used to order the words of the vector).


The Princeton WordNet contains information about nouns, verbs, adjectives and adverbs in English and is organized around the notion of a synset.

to which extent each knowledge base provides new knowledge not provided by the others.


Many types of co-occurrence exist other than noun-noun.

automatic thesaurus construction (Curran, 2002) offers great potential for various useful NLP applications.


However, the experiments did not show that this can improve retrieval effectiveness.

even if we can detect one occurrence of a compound, we may miss others that use its variants.


Web search), we do not have a static document collection from which relations can be extracted.

then we will describe our general IR models and the extraction of term relations.


All documents have been processed in a standard manner: terms are stemmed using Porter stemmer and stopwords are removed.

the requirement for query expansion is less than word sense disambiguation: we do not need to know the exact word sense to make expansion.


For the last set of experiments, we used the probabilistic model described in (Collins, 1999) (model 2), and the Tree Kernel (Collins and Duffy, 2002).

use of probability estimates is not a serious limitation of this approach because in practice candidates are normally provided by some probabilistic model and its probability estimates are used as additional features in the reranker Shen and Joshi, 2003;.


In the other two sets of experiments, described below, we test our best model on the standard Wall Street Journal parsing benchmark (Collins, 1999) with the Voted Perceptron algorithm as the learner.

the expected loss of choosing a member of the group will be smaller than that for the outlier.


Each row of Y corresponds to a context vector of entity pair, and the above process can be considered as transforming the original context vectors in a d-dimensional space to new context vectors in the K-dimensional space.

table 3 shows that all of the four categories of features contribute to the improvement of performance more or less.


(2004)'s method1 which only uses word feature.

it is time-consuming and labor-intensive to manually tag a large amount of training data.


Additionally, many discriminative models use a generative model as a base model and add discriminative features with reranking (Collins, 2000;Charniak and Johnson, 2005;Roark et al., 2004), or train discriminatively a small set of weights for features which are generatively estimated probabilities (Raina et al., 2004;Och and Ney, 2002).

we define Bayesian Networks over the m input variables X 1 , X 2 , .


These results also suggest new directions of research within this model.

the performance of state of the art WSD systems on realistic evaluations is only comparable to the "first sense" baseline (cf.


We believe the improved precision of the cluster compatibility method explains this trend as well.

we model the conditional distribution where f k is a binary feature function that computes attributes over the field sets, and Λ = {λ k } is the set of real-valued weights that are the parameters of the maximum-entropy model.


We employ our model to perform sentence compression throughout a whole document (by compressing sentences sequentially) and evaluate whether the resulting text is understandable and informative using a question-answering task.

their aim is to preserve the meaning and structure of the original sentence as much as possible.


Recent years have witnessed significant progress in developing methods for the automatic identification and labeling of semantic roles conveyed by sentential constituents.

this is motivated by much research in lexical semantics (e.g., Levin (1993)) hypothesizing that the behavior of words, particularly with respect to the expression and interpretation of their arguments, is to a large extent determined by their meaning.


We remedy this shortcoming by treating the semantic role assignment as a global optimization problem.

we used all dependency relations provided by MiniPar (42 in total).


For example, in Q: Who beat Floyd Patterson to take the title away?, beat, take away, and title are identified as predicate candidates and beat is selected the main predicate of the question.

we wanted to assess whether questions and answers often evoke the same or related frames (with similar roles).


The base case, for leaves of τ q , is: Note that k ranges over indices of answer-words to be aligned to q j .

relatively fewer attempts have been made in the QA community.


Table 2: Perplexities of the alignment models.

all entries marked with an asterisk are better than the None baseline with 95% statistical significance computed using paired bootstrap resampling (Koehn, 2004).


All the bridge languages yield lower BLEU/higher AER relative to the No-Bridge baseline.

various feature functions (Och and Ney, 2004) are then computed over the entries in the phrase table.


In the next two sections, we describe how posterior probabilities can be used to a) construct alignment systems from a bridge language, and b) merge several alignment systems.

we did not investigate these choices in this paper.


we use posterior probabilities as the interface between different alignment models.


This work was partially supported under the GALE program of the Defense Advanced Research Projects Agency, Contract No.

with their approaches, we have a very flat, one-level notion of dependency, which is bilingually motivated and learned automatically from the parallel corpus.


As described below, the training of our new WSD model, though not incorporated within the EM training, is also far more closely tied to the SMT model than is the case with traditional standalone WSD models.

instead, translation disambiguation decisions are made indirectly, typically using only word surface forms and very local contextual information, forgoing the much richer linguistic information that WSD systems typically take advantage of.


It is quite possible that distinct "senses" in our WSD for SMT system could be considered synonyms in a traditional WSD framework, especially in monolingual WSD.

our WSD-based lexical choice models are designed to directly model the lexical choice in the actual translation direction, and take full advantage of not residing strictly within the Bayesian sourcechannel model in order to benefit from the much richer Senseval-style feature set this facilitates.


McDonald (2006) also presents a sentence compression model that uses a discriminative large margin algorithm.

g: In a conference call with analysts the bank said it boosted credit card reserves by $350 million.


Disjunctive descriptions were a majority in either condition, and most of these were nonaggregated.

this evidence suggests that speakers conceptualise referents as gestalts (Pechmann, 1989) whose core is their basiclevel tYPE (Murphy, 2002) and some other salient attributes like COLOUR.


Laplace's formula defines the determinant as a linear combination of matrix cofactors of an arbitrary row j: It should be clear that any c j,k is constant with respect to the cell [A] j,i (since it is formed by removing row j of A) and that other entries of A are constant with respect to the cell [A] j,i .

moving to the nonprojective case, there are two difficulties: (a) we must marginalize over nonprojective trees and (b) we must define a generative model over (X, Y).


A naïve method for computing the partition function is therefore to evaluate The above would require calculating n determinants, resulting in O(n 4 ) complexity.

more concretely, this technique corresponds to defining the matrixL(θ) aŝ where diag(v) is the diagonal matrix with the vector v on its diagonal.


While coverage is the same, F and crossbrackting scores are marginally improved, and the dependency score is marginally deteriorated.

using stratified shuffling 1 , we performed a t-test on precision and recall, and found both to be significantly worse in the inclusion condition.


Using 5 folds, each model is trained on 80% of the data while the remaining 20% are held out.

the overall performance metrics of the parser are calculated on the aggregated totals of the five held out test sets.


These results clearly indicate that the inclusion entity model is able to leverage the additional information about English inclusions provided by our inclusion classifier.

on a 10,000 word subset of the neologism archive (Wangensteen, 2002), the best method of combining character n-grams and regular expression matching yields an accuracy of 96.32% and an F-score of 59.4 (P = 75.8%, R = 48.8%).


To overcome the poor accuracy of parsing inclusions, we developed two methods for interfacing the parser with an existing annotation-free inclusion detection system.

a slightly lower performance and/or coverage is anticipated, albeit the type and domain as well as the annotation of both the NE-GRA and the TIGER treebanks are very similar.


Knowing the directionality of rules here could help in making a query more general or specific depending on the user needs.

they also make no attempt to distinguish between symmetric and asymmetric inference rules.


Feature analysis revealed that deverbal adjectives derived from stative verbs are more similar to basic adjectives than those derived from process-denoting verbs.

feature examination showed that the distributional differences between basic and event adjectives are not robust.


A proper treatment of polysemy is essential in the area of lexical acquisition, since polysemy repre-sents a pervasive phenomenon in natural language.

as a Gold Standard for the experiments to follow, 210 Catalan adjectives were classified by three experts.


The general nature of the architecture should be useful for related tasks that involve polysemy within the area of automatic lexical acquisition.

this mapping cannot distinguish deverbal adjectives with a basic meaning (11 basic and 6 BE adjectives in the Gold Standard).


Choose an appropriate P Ideally, we'd like to do as little pruning as possible and we'd like to use as large a P as possible, subject to the memory allocation.

they take some probability mass from the rich n-grams that have been seen in training and give it to poor unseen n-grams that 2 http://www.speech.cs.cmu.edu/SLM might appear in test.


Take the first differences (which are modeled as interarrivals of a Poisson process) 7.

where −2 −1 > 0 simply says that the trigram was seen in training data.


Because p L does not predict trees, p G still "gets to choose" the syntax tree, but in the limit it must find a tree for This is effectively the morphology-first pipeline.

they assumed that the input to the parser was already (perfectly) morphologically disambiguated.


In contrast to existing algorithms developed for this problem, our algorithm is designed to (1) operate under a resource-scarce setting in which no languagespecific tools or resources are available and (2) more tightly integrate morphological information with the distributional POS induction framework.

when running his program, we use wSJ and Prothom Alo as the input corpora.


Words that are labeled with a confidence value that exceeds the current threshold (which is initially set to 1 and -1 for positively and negatively labeled instances, respectively) will be 10 As a related note, Clark's (2001) bootstrapping algorithm uses KL-divergence to measure the distributional similarity between an unlabeled word and a labeled word, adding to a cluster the words that are most similar to its current member.

to existing algorithms developed for this problem, our algorithm is designed to (1) operate under a resource-scarce setting in which no languagespecific tools or resources are available and (2) more tightly integrate morphological information with the distributional POS induction framework.


The results that we report are the averages over 10 such random runs for each labeled training set size.

0.5) and classifying the instances above the threshold as positive and others as negative.


Note that since edit decisions are based in part on (arbitrary) features of the target sequence, and since generation involves construction of the target sequence, it is not uncommon for a greedy generator to make edit decisions which are locally optimal, but which result several edits later in a partially constructed sequence in which no good edits are available.

1: Given a set S of source sequences 2: V ← [], an empty list 3: α ← 0, a weight vector 4: for some number of iterations do for s in S do 6: Pick t, t , t having higher affinity with s 7: end if Append α to V 13: end for 14: end for 15: Return the mean α from V Table 1: The training algorithm.


Among all possible target language character sequences, we will choose the character sequence with the highest probability: The posterior probability Pr(t n 1 |s m 1 ) is modeled directly using a log-linear combination of several models (Och and Ney, 2002), including a characterbased phrase translation model, a character-based lexicon model, a 4-gram character sequence model, a character penalty and a phrase penalty.

we can improve on both systems by combining them.


In the multi-class setting, supervised learning approaches can be used.

to this purpose, we implemented a voting mechanism.


For example, the phrase pair "after-sale service <-> 售后服务 (shouhoufuwu)" is added; (2) The probability distribution of the phrase pairs is changed.

the translation results are shown in table 3.


We also manually evaluated the RBMT systems and SMT systems in terms of both adequacy and fluency as defined in (Koehn and Monz, 2006).

much important work continues to be carried out in Example-Based Machine Translation (EBMT) (Carl et al., 2005;Way and Gough, 2005), and many existing commercial systems are rule-based.


krishnan and Manning (2006) divided the model into two CRFs, where the second model uses the output of the first as a kind of non-local information.


After the training has finished, At first glance, this seems sufficient condition for good models.

having described the basic perceptron algorithms, we will know explain our algorithm that learns the weights of local and non-local features in a unified way.


Discriminative methods such as Conditional Random Fields (CRFs) (Lafferty et al., 2001), Semi-Markov Random Fields (Sarawagi and Cohen, 2004), and perceptrons (Collins, 2002a) have been popular approaches for sequence labeling because of their excellent performance, which is mainly due to their ability to incorporate many kinds of overlapping and non-independent features.

note that if the features are all local, the secondbest candidate (generally n-best candidates) can also be found efficiently by using an A* search that uses the best scores calculated during a Viterbi search as the heuristic estimation (Soong and Huang, 1991).


The aim of the learning algorithm is to obtain an appropriate weight vector, α, given training set The learning algorithm, which is illustrated in Collins (2002a), proceeds as follows.

4 it is very time consuming to run the complete training process several times.


The highest performance with the proposed algorithm was achieved when n ′ = 6400, where the improvement due to non-local features became 0.74 points.

collins (2002a) also proposed an averaged perceptron, where the final weight vector is Algorithm 3.1: Perceptron with margin for sequence labeling (parameters: c) the average of all weight vectors during training.


Finally, we compared our algorithm with the reranking approach (Collins and Duffy, 2002;Collins, 2002b), where we first generate the n-best candidates using a model with only local features (the first model) and then re-rank the candidates using a model with non-local features (the second model).

cRFs or perceptrons generally have nearly zero error on the training data, although the first model should mis-label 11 The weights were re-trained for the second model.


We use the cosine function for similarity and test it on automatically classifying 120 target words from four regions, using different datasets for the extraction of feature vectors.

the demand on cross-cultural knowledge thus poses a challenge for building a Pan-Chinese lexical resource manually.


Product features (e.g., "image quality" for digital camera) in a review are good indicators of review quality.

usually a best review could be taken as the main reference that users only need to read before making their purchase decision on a certain product.


Our contribution can be summarized in two-fold: (1) we discovered three types of biases in the ground-truth used extensively in the existing work, and proposed a specification on the quality of product reviews.

it is crucial to have a mechanism capable of assessing the quality of reviews and detecting low-quality/noisy reviews.


This will not call for extra training time and memory for the whole training process.

we also reduce the scale of the training data at the same time.


The co-occurrence statistics for PMI are typically computed using a sliding window.

these approaches use word repetitions to evaluate coherence.


Figure 1 plots the length of the summaries (averaged over the two manual summaries) with respect to the length of the books.

an impediment we encountered was the size of the graphs, which become intractably large and dense for very large documents such as books.


The accuracy of the C&C tagger increases rapidly, and the accuracy exceeds our automated method on PPCME Test with just 50 labeled sentences and on the PPCME Wycliffe with 400 examples.

this places Wycliffe's Bible as far back as possible without introducing extreme nominal and verbal inflections in word alignment.


This task is more difficult to approximate, e.g., using frequency information.

the resulting sparseness affects all model families, since all ultimately rely on co-occurrences.


In preliminary experiments, we found that vector spaces with context words as basis elements are in fact unable to distinguish such word pairs reliably.

sections 4 and 5 present our experimental setup and results.


The strategy of our model to derive generalisations directly from corpus data, without recourse to resources, is similar to another family of corpus-driven selectional preference models, namely EM-based clustering models (Rooth et al., 1999).

the success of our method depends directly on the topology of the vector space.


Our model is competitive with existing "deep" models while exhibiting a higher coverage.

this similarity is not sensitive to selectional preferences over specific semantic relations, and thus cannot model the plausibility data we are interested in.


All evaluated measures satisfy P4 and P7.

the former situation is presented in the figures A and B in figure 2.


They are particularly appealing for tasks in which there is an abundance of language data available, but manual annotation of this data is very resource-intensive.

v-measure has another advantage as a clustering evaluation measure over v I and Q 0 .


The F-Measure of both of these clustering solutions in 0.6.

in this paper, we describe a new entropy-based external cluster evaluation measure, V-MEASURE 1 , designed to address the problem of quantifying such imperfection.


Notice that the computations of homogeneity, completeness and V-measure are completely independent of the number of classes, the number of clusters, the size of the data set and the clustering algorithm used.

dom's Q 0 measure (dom, 2001) uses conditional entropy, H(C|K) to calculate the goodness of a clustering solution.


The cost term captures some of this information, since a partition with fewer clusters is likely to be more complete than a clustering solution with more clusters.

using these results as an example, we find that at approximately 30 clusters an increase of clusters translates to an increase in V-Measure.


We again (see section 5.1) observe the close dependency of VI and k. Moreover, in considering figures 5 and 4, simultaneously, we see considerably higher values achieved by the document clustering experiments.

in order for the goodness of two clustering solutions to be compared using one these measures, an identical post-processing algorithm must be used.


this is because the ranking of words depends on assigned topics, concretely, mixture ratios of assigned topics.


Despite these encouraging results, we believe that additional research on improved estimation of probabilities in N-gram-or phrase-based statistical machine translation systems is needed.

this suggests that most of the alternative translation hypothesis result in word reorderings and not in many alternative word choices.


In the baseline system, this feature consists of a 4-gram back-off model of words, which is trained from the target side of the bilingual corpus.

of these constraints, only one segmentation is possible for a given sentence pair.


This can be problematic when the data is sparse.

the problem of generalization to new translations seems to be promising to us.


From these results, seg(1, 2) and seg(10, 15) are detected as trigger segments.

acc(categorizer(D k )) will decrease if no features that contribute to the categorization are in D k .


Table 4 shows the precision as the results of the word sense disambiguation on the combination of LXD-DEF and LXD-EX (LXD-ALL).

in Japanese, relative clauses can have gapped and non-gapped readings.


(2003) using a combination of simple Bayes learners).

these have about 75,000 definition and 46,000 example sentences respectively.


Though this is probably the most reliable way, it is impractical for many scenarios since assembling and manually annotating a representative gold standard may already be quite a laborious task.

this is certainly impractical since after each AL iteration a new committee of classifiers has to be trained causing unwarranted annotation idle time.


Generally, it would be expected that using a wordnet is the best solution if we are interested in an isalike relation between two words.

for the final system, we back off from the most precise information sources to the less precise.


Similar to Clarkson and Robinson (1997), we implemented an exponentially decaying cache of length l (usually between 100 and 1000), using the following decay function for a word w i and its position p in the cache.

using the results of our 8 samples, we performed paired t tests for every method with the baseline as well as with the cache model.


During translation decoding, the domain specific language model was used as an additional feature in the log-linear combination according to the PHARAOH decoder's option.

domain specific models are then created from the data contained in the sub-corpora in a batch process.


The improvements over the baseline of our method in both BLEU and NIST (Doddington 2002) score were greater than those for both CLM and SMix.

the size of the bilingual training corpus was 2.9M sentence pairs.


It was shown that MBR outperforms MAP decoding for different evaluation criteria.

here, we will present additional evidence that MBR decoding is preferable over MAP decoding.


Except for the MERT, the training criteria that we will consider are additive at the sentence-level.

the improvement over a state-of-the-art MERt baseline is 1.3% Bleu absolute for the MAP decision rule and 1.1% Bleu absolute for the MBR decision rule for the large Chinese-English tC-Star speech translation task.


the N best translation hypotheses of the MT system.

we will show that some achieve significantly better results than the standard minimum error rate training of (Och, 2003).


The first nine grammar rules are used to constrain phrase reor-dering during phrase combination.

the former reordering case with the smaller distance is usually referred as local reordering and the latter with the longer distance reordering as global reordering.


The ratio r of the number of negative training examples to the number of positive training examples is 29.4.

we then retrain the best model with all data in the training data set, and apply it to the blind test set.


A zero pronoun (ZP) is a gap in a sentence which refers to an entity that supplies the necessary information for interpreting the gap.

we only attempt to resolve a coreferential zero pronoun to noun phrases preceding it.


Using • to indicate left-most substitution, we can write: • t 1 is a root-subtree of t 1 , and t 1 is a root-subtree of t 2 , if ∃t 3 , such that t 3 • t 1 = t 2 ; • t 1 is a frontier-subtree of t 1 , and t 1 is a frontier- • t is the x-frontier-subtree of t, t = f s x is a set of nodes in t, such that if t is pruned at each i ∈ x it equals t .

given an initial setting of the parameters (all depth 1 elementary trees at their empirical frequency), the method calculates the expected frequency of all complete and incomplete trees.


The top-10 overrepresented subtrees in the WSJ20-corpus are given in figure 1.

unlike those existing DOP models, the score distribution over these elementary trees is extremely skewed: relatively few trees receive high scores, and there is a very long tail of trees with low scores.


We compute a word-pair's entropy by considering it as a distribution over features, in a manner that is analogous to the feature entropy defined in (Turney, 2006).

in this study, we present a novel system for generating lexical analogies directly from a text corpus without relying on dictionaries or other semantic resources.


We know that words that occur close to a target word tend to be good indicators of its intended sense.

our future work will explore other tasks such as information retrieval and text categorization.


a river bank versus a financial institution), and a single synset can be represented by multiple synonymous words, such as "middle" and "center."

our edge weights are the SemCor frequency counts for each target Synset, with pseudo-counts of .1 for all Synsets, 1 for first sense of each word, and .1 for the first word in each Synset.


WordNet explicitly marks semantic relationships between synsets, but we are additionally interested in representing relatedness between words.

zKL's re-estimate for the zero terms adds little information.


if there is a wide range in the exponent of noisy terms, the maximum possible penalty to such terms ascribed by skew divergence may be beneficial.


The use of ancestor-descendant relations makes it possible to capture the characteristics which cannot be captured by parent-child relations alone.

we focus on ancestor-descendant relations to compensate for the weakness.


In ANCESTOR METHOD, both instances are positive instances.

mODEL ANCESTOR uses ordered pairs of an ancestor node and a descendant node as positive instances and the other ordered pairs as negative instances.


When the ancestor-descendant relation is used, both of the above two instances for nodes 3 and 4 can be considered as positive instances.

the type C is not available in the proposed method because the proposed method analyzes a sentence backwards unlike Kudo and Matsumoto (2002).


The score can be regarded as likeliness of the two nodes being in the parent-child (or the ancestordescendant).

consider the following two instances in Figure 1 as an example: the ordered pair of ID 3(pizza-and) and ID 5(ate), and the ordered pair of ID 4(salad-accusative) and ID 5.


The receiver infers the parent node from the received sequence (string output) in consideration of the codewords that can be transmitted (string[k]).

we propose a method in which the problem of determining the modifiee of a chunk is regarded as a kind of a coding problem: dependency is expressed as a sequence of values, each of which denotes whether a parent-child relation or an ancestordescendant relation holds between two chunks.


The approaches assign a unique n-bit codeword to each class, and then n classifiers are trained to predict each bit.

only the ordered pair of ID 4 and ID 5 is a positive instance in PARENT METHOD.


ID 5 is determined as the modifiee of ID 1 only after the relations with each node of ID 2, 3 and 4 are recognized not to be ancestor-descendant.

their processes are essentially the same as the process in PARENT METHOD.


Even if word-by-word alignment is uncertain, the boundaries of conjuncts are often obvious, and it is also much easier for human annotators to mark only the beginning and end of each conjunct.

our next method, called "box-based," is designed on the following heuristic.


In addition, the corpus contains an explicit tag "COOD" for marking coordinations.

there is no use in discriminating between these two, and one of them, namely (Delete, Insert), is prohibited.


µ n | P ∈ Π −Π , ν ∈ pset(P ) and ∀ i∈[1..n] µ i ∈ powerset(Π)}.


Their removal surprisingly improves both PARSEVAL and LA results.

it neither weights results, differentiating between linguistically more or less severe errors, nor does it give credit to constituents where the syntactic categories have been recognised correctly but the phrase boundary is slightly wrong.


The TIGER treebank and the TüBa-D/Z both contain newspaper text, but from different German newspapers.

to Kübler (2005) and Maier (2006), who converted the treebank before extracting the grammars in order to measure the impact of single features like topological fields or unary nodes on PCFG parsing, we convert the trees in the parser output of a parser trained on the original unconverted treebank resources.


If we have a good bound on the maximal sequence length, this can be dealt with efficiently.

then, one can argue that optimizing a loss function that incorporates this cost can lead to better generalization properties.


these features can encode similarities between two sequence segments of arbitrary lengths, which can be very useful in tasks such as APS.


In model P, we ordered the anchor texts in a linear-chain sequence.

the potentials are factorized according to the set of features {f k }.


The results of our experiment suggest that MavenRank can indeed be used to find central speakers in a corpus of speeches.

our results show that MavenRank scores are largely driven by committee status in most topics, but can capture speaker centrality in topics where speeches are used to indicate ideological position instead of influence legislation.


This is not a major problem because even if a one to one map between topics and committees existed, speakers from outside a topic's related committee are free to participate in the topic simply by giving a speech.

the first are highly politicized topics for which speeches are intended not to influence  Alternative models, using measures of centrality based on the centroid were also examined.


Abney simply uses a uniform distribution over labels (e.g., parses), to reflect that the label is unknown.

abney defines a function K that is an upper bound on the negative log-likelihood, and shows his bootstrapping algorithms locally minimize K. We now present a generalization of abney's K function and relate it to another semi-supervised learning technique, entropy regularization (Brand, 1999;Grandvalet and Bengio, 2005;Jiao et al., 2006).


This section describes an approach to learning in our model.

5 For this reason the training problem is a hidden-variable problem, where the training examples contain only partial information, and the CCG lexicon and parse derivations must be learned without direct supervision.


The use of a detailed grammatical formalism such as CCG has the advantage that it allows a system to handle quite complex semantic effects, such as coordination or scoping phenomena.

a second set of new combinators are the relaxed functional composition rules: These rules are variantions of the standard functional composition rules, where the slashes of the principal categories are reversed.


We adapt the HDP machinery to tie these binary production distributions together.

to cope with unknown words, we replace any word appearing fewer than 5 times in the training set with one of 50 unknown word tokens derived from 10 word-form features.


Most previous inference algorithms for DP-based models involve sampling (Escobar and West, 1995;Teh et al., 2006).

to draw β ∼ GEM(α), we first generate a countably infinite collection of stick-breaking proportions u 1 , u 2 , .


[49][50]  Next, we can compare each All row with the Rel row immediately below it.

we ran this selftraining procedure for three iterations and then used the resulting classifier as our relevant sentence classifier in the IE experiments described in Section 6.3.


Most physical targets fall into one of two general semantic categories: BUILDING or VEHICLE.

the classifier is then retrained with all of the relevant sentences and an equal number of irrelevant sentences, and the process repeats.


Next, we evaluated the performance of AutoSlog-TS' extraction patterns when they are applied only in the sentences deemed to be relevant by our relevant sentence classifier.

all sentences in an irrelevant document must be irrelevant, so these sentences form our initial irrelevant sentences pool.


In Section 3, we describe the self-trained sentence classifier, which requires only a few seed patterns and relevant and irrelevant documents for training.

sometimes a general se-mantic class can be partitioned into subclasses that are associated with different roles.


For example, one role in the terrorism domain is physical target, which refers to physical objects that are the target of an attack.

this results in an asymmetry in the training set.


Among them, feature-based methods (Kambhatla 2004;Zhou et al., 2005) achieve certain success by employing a large amount of diverse linguistic features, varying from lexical knowledge, e ntityrelated information to syntactic parse trees, dependency trees and semantic information.

for example, "got married" is critical to determine the relationship between "John" and "Mary" in the sentence "John and Mary got married… " as shown in figure 1(e), and the information contained in SPT ("John and Mary") is not enough to determine their relationship.


"Jane" and " ABC news" in the sentence "Jane, ABC news, California."

it is always a good idea to integrate them via a composite kernel.


Syntax translation models acquired by composing treebank grammar rules assume that long rewrites are not decomposable into smaller steps.

in (Galley et al., 2006), a composed rule is extracted only if the number of internal nodes it contains does not exceed a limit (i.e., 4), similar to the phrase length limit in phrase-based systems.


Because basic and will are unaligned, ATS will learn no phrase pairs that translate to these words alone, though they will be learned as a part of larger phrases.

the GHKM extractor learns translation rules from an aligned parallel corpus where the target side has been parsed.


We have shown that it is possible to gain insights from the strengths of the phrase-based extraction model to increase both the phrasal coverage and translation accuracy of the syntax-based model.

because of this, they potentially apply to any sentence.


A single oracle with 1-best translation is analytically solved without a QP-solver and is represented as the following perceptron-like update (Shimizu and Haas, 2006): Intuitively, the update amount is controlled by the margin and the loss between the correct and incorrect translations and by the closeness of two translations in terms of feature vectors.

insertion features are integrated in which no word alignment is associated in the target.


The MERT approach seems to be confused with the slightly larger data and with the mixed domains from different epochs.

the improvement is merely relative to the baseline translation system, namely whether or not there is a good translation in their k-best.


Concretely, if we find that the current classifier already has acceptably strong confidence on its classification results for all remained unlabeled data, we assume the current training data is sufficient to train the classifier with maximum effectiveness.

in practice, for WSD, the costs of various senses of a disambiguated word are unequal and unknown, and they are difficult to evaluate in the process of learning.


There is a recent variant of over-sampling named SMOTE (Chawla et al., 2002) which is a synthetic minority over-sampling technique.

to our best knowledge, there has been no such attempt to consider the class imbalance problem in the process of active learning for WSD tasks.


For CRF, we used the Gaussian prior as the second term on the RHS in Equation 1, where δ 2 represents the hyper-parameter in the Gaussian prior.

for the features of the generative models, we used all of the feature types shown in figure 1.


The gradient of Equation (1) can be written as follows: Calculating E p(Y|x,λ) as well as the partition function Z(x) is not always tractable.

as shown in Tables 3 and 4, HySOL signifi-1 Chunking (CoNLL-2000) data has no common development set.


Let λ be a parameter vector and f (y s−1 , y s , x) be a (local) feature vector obtained from the corresponding position s given x. CRFs define the conditional probability, p(y|x), as being proportional to a product of potential functions on the cliques.

with respect to the performance of NER and Chunking tasks, the current best performance is reported in (Ando and Zhang, 2005), which we refer to as 'ASO-semi', as shown in Figures 5 and 6.


ASOsemi also incorporates unlabeled data solely for the additional information in the same way as our method.

only a forward-backward algorithm is required for the efficient calculation of our parameter estimation process.


set) that ASO-semi does not use in training.

our framework can also be seen as an extension of LOP-CRFs that enables us to incorporate unlabeled data.


(2005) performed speaker recognition by computing the expected counts of phone bigrams in a phone lattice, and estimating an unsmoothed probability distribution of phone bigrams.

(2006)'s vector space latticebased retrieval method.


A speech recognizer then generates a lattice for each speech segment.

this is still not the case for SDR on data which are more challenging, such as conversational speech in noisy environments, as the 1-best transcripts of these data contain too many recognition errors to be useful for retrieval.


The CRF model addresses the problem of labeling sequential tokens while relaxing the strong independence assumptions of Hidden Markov Models (HMMs) and avoiding the presence of label bias from having few successor states.

in our case, we care about the four types of attributes of each data record (one TTE experiment).


In addition, semantic homology might not include any orthographic clues (e.g., genotoxic stress and DNA damage).

the Viterbi many-to-many pairwise alignments are then generated by combining equivalent pairs of many-to-one alignments using three different standard symmetrization methods for word-alignment-union, intersection, and the refined method of Och & Ney (2003).


Ultimately, the success of an MCA algorithm should be judged by its effect on the success of the citance analysis systems that use MCAs as their input.

2003) and that Chk1 is required for Cdc25A ubiquitination by SCF β-TRCP in vitro, we explored the role of Cdc25A phosphorylation in the ubiquitination process."


For the Viterbi alignments, only three results could be generated (one for each symmetrization method).

a multiple citance alignment (MCa) is an equivalence relation ∼ ij ∼ ij + on the set i c i , which is the transitive closure of the union of all pairwise alignments of citance pairs in G. Taking the transitive closure and not only the union of all pairwise alignments ensures that the MCa is an equivalence relation.


We modified the CRF alignment system of Blunsom & Cohn (2006) to support MCA by incorporating the posterior decoding algorithm from Section 3.2 into the existing system.

unlike Viterbi, posterior-decoding produces alignments with much higher recall levels, by increasing the match-factor and decreasing the gap-factor.


The average network latency per batch is 35 milliseconds, yielding a total latency of 0.8 seconds caused by the distributed language model for an average sentence of 22 words.

figure 4 shows perplexities for models with Kneser-Ney smoothing.


As a general rule, more data tends to yield better language models.

they don't report details of the integration or the efficiency of the approach.


With larger training material, spurious analogies become more likely.

the response rate is measured as the percentage of words for which we do have at least one translation produced (correct or not).


Note that this is an exact method which follows from the property (Lepage, 1998): This leaves us with a quadratic computation time which is still intractable in our case.

this allows to capture 1:n relations such as dépasseront↔will exceed, where the future tense of the French word is adequately rendered by the modal will in English.


Analogical inference can be applied to the task of extending an existing bilingual lexicon (or transfer table) with new entries.

we apply a second strategy which consists in computing the analogical equations [S i : S j = S :?]


However, in the automaticallyaligned case, many vowel frames with substantial consontant coloring are re-allocated to those adjacent consonants, giving more complex consonants, but comparatively less complex vowels.

indeed, our best model achieves a surprising performance of 21.4%, greatly outperforming other generative methods and achieving performance competitive with state-of-the-art discriminative methods.


Incorrect prior splits can needlessly fragment training data and incorrect prior tying can limit the model's expressivity.

different natural classes emerge in this case, with, for example, preceding states partitioned into front/high vowels vs. rounded vowels vs. other vowels vs. consonants.


The oldest phonetic key function is apparently Soundex, which was patented in 1918 and 1922 by Russell and Odell (U.S.

under this approach a key function can be learned even if the similarity model is unknown, provided that sufficient equivalence classes are available.


The following rows contain information about whether lemmas are available, the number of coarse-and fine-grained part-of-speech tags, the number of feature components, and the number of dependency labels.

the tree that maximizes the sum of these combined scores is taken as the final output parse.


The closed class requires a system to use only those resources provided as part of the shared task.

two other entries used MIRA (Mannem, 2007) or online passive-aggressive learning (Johansson and Nugues, 2007b) to train a globally normalized model.


slightly better results were obtained by not splitting the training data into smaller sets; for the remaining languages, accuracy was improved by using the CPOSTAG of Next as the defining feature for the split (instead of POSTAG).

hence, children of the root node in the final output may not have been predicted as such by the treebank-induced classifier.


Phrases are limited to 8 tokens in length (n, m ≤ 8).

instead of selecting phrase pairs with a p-value less than exp(−20), we will select phrase pairs with a negative-log-p-value greater than 20.


However, the suffix array returns matchings in lexicographical order, not numeric order.

we cannot easily obtain a list of these in advance, but we observe that they always consist of a pair u and v of patterns from near the top of the frequency list.


In the worst cases a single collocation took several seconds to compute.

it cannot be used for discontiguous substrings.


To see why the latter optimization is useful, consider a phrase abXcd.

node (1)'s suffix is represented by the node pointed to by the d-edge from node (3), which is node (4).


To accomplish this, one of the original candidate translations, e.g.

in a first experiment, we trained approximately 200 systems using different parameter settings in training.


For the purpose of expanding the given name, N, into effective queries to search and extract transliterations T, we define a probabilistic function for mapping prefix syllable from the source to the target languages.

to find transliterations of a name, a promising approach is to automatically expand the given name into a query with the additional requirement of some morpheme expected to be part of relevant transliterations that might appear on the Web.


Nevertheless, it is worth noting that the improvement of Bayesian network approach over its baseline is very significant (+5.5%).

lDA is a probabilistic model for collections of discrete data and has been used in document modeling and text classification.


Tag) 73.6 SVM-topic 73.0 SVM baseline 72.4 NB baseline 69.8 ASO (Ando, 2006) 74.1 SVM-LSA (Strapparava et al., 2004) 73.3 Senseval-3 Best System (Grozea, 2004) 72.9 Table 5: Results compared to previous best systems on Senseval-3 English lexical sample task.

both methods have information loss and are disadvantaged against the Soft Tagging approach.


In our experiment, we achieved a similar coverage improvement with only 21 new entries for 30 different MWE candidates, with an average of 0.7 entries per MWE.

since we are only interested in acquiring new lexical entries for MWEs which are not covered by the grammar, we used the error mining results van Noord, 2004) to only keep those candidates with parsability ≤ 0.1.


One of the main assumptions in our use of different models based on the same algorithm is that while the output generated by those models may often differ, agreement between the models is an indication of correctness.

if, instead, this procedure returns a list of several possible actions with corresponding probabilities, we can then parse with a model similar to the probabilistic LR models described by Briscoe and Carroll (1993), where the probability of a parse tree is the product of the probabilities of each of the actions taken in its derivation.


An example is the above BIO NP, in which the phrase "glutathione transferase P1-1" is an appositive indicating which "enzyme" is meant.

our suspicions are supported by the observation that no team was able to improve target domain performance substantially over a state of the art baseline.


In parsing adaptation, the former corresponds to a difference between the features seen in each domain, such as new words in the target domain.

this approach is unsatisfying as it does not allow general purpose adaptation.


in this paper, we refer to this information extractionoriented task as opinion extraction.


Kim and Hovy (2006) proposed a method for extracting opinion holders, topics and opinion words, in which they use semantic role labeling as an intermediate step to label opinion holders and topics.

the former task can be regarded as a special type of predicate-argument structure analysis or semantic role labeling.


First, it assumes the availability of a list of potential aspect expressions as well as evaluation expressions; however creating such a list of aspects for a variety of domains can be prohibitively expensive because of the domain dependency of aspect expressions.

context-independent statistical clues: Statistics such as aspect-aspect and aspect-evaluation In what follows, we describe our method for aspect-evaluation.


our method does not require any aspect lexicon.


Finally, the dual and masculine sound plural do not express nunation.

in the next section, we describe all relevant linguistic facts of case in Arabic.


The first and second rows list the errors made by the statistical and rule-based systems, respectively.

we believe that our tool can serve an important function in improving the treebank annotation.


These abstracts cover topics more broadly in Biomedicine and not just molecular biology.

when taggers developed in one domain are used in another domain, the performance can degrade considerably.


These labels were reduced to 42 in the version used for CoNNL 2007, in order to make it comparable to other corpora.

for the domain adaptation track we applied a tree revision method which learns how to correct the mistakes made by the base parser on the adaptation domain.


in general such solvers have a time complexity exponential in the  Canisius et al.


We used Nivre's algorithm (Nivre et al., 2006), which is a variant of the shift-reduce parser.

the beam search allows us to find a reasonable approximation.


We performed a beam search by carrying out a K-best search through the set of possible sequences of actions as proposed by Johansson and Nugues (2006).

in Nivre's parsing algorithm the dependency graph can be viewed as a graph resulting from a set of parsing decisions (in this case Shift, Reduce , Left & Right) made, starting with the initial configuration (nil, W, ∅) .


Moreover, with larger values of K, there was a decrease in the parsing accuracy.

in Nivre's algorithm, as there is no one to one correspondence between parsing decisions and the graph edges, the number of errors in the edges can't be used as a loss function as it won't reflect the exact loss in the parsing decisions.


The experiment showed that given the grammar model we have adopted it does matter which version of the algorithm one uses.

the Greenberg's basic word order typology cannot shed enough light into the performance of the three parsing algorithms.


We assume that every possible headdependent pair ¨ is described by a feature vector Eisner's algorithm achieves optimal tree packing by storing partial structures in two matrices and .

in the extended model, is updated as defined in eq.


classif ierprep(e) = sign(wprepφ(e)) classif ier det (e) = sign(w det φ(e)) classif ier hv (e) = sign(w hv φ(e)) The input to the above classifiers is an edge e instead of a whole sentence x. φ is a mapping from an edge to a feature vector.

informally, the algorithm has each vertex in the graph greedily select the incoming edge with highest weight.


As observed by (Isozaki et al., 2004), incorporating both top-down (root find) and bottomup (base-NP) can yield better improvement over the Yamada's parsing algorithm.

in this way, we can simply adopt a sequential tagger to auto-assign the relation labels after the whole sentence is parsed.


By adopting the projectivized en/de-coding over the modified Yamada's algorithm, we can treat the words that do not have a parent as roots.

the experimental results are mainly evaluated by the unlabeled and labeled attachment scores.


Unlike last year, we did separate the action prediction and the label recognition into two stages where the one of the learners could provide more information to another.

instead of pre-determining the root and base-phrase structures, the tagging results of the neighbor and root parsers were included as new features to add wider information for the shift-reduce parser.


With regard to the normalization term Z(x), as equation 2shows, naive computation requires summing over all assignments of y.

the first corpus, C RCG , consists of the raw output of ASR (figure 1), the other one, C COR , contains the corresponding corrected and formatted reports (figure 2).


Finally, the work of Ye and Viola (2004) bears similarities to ours.

we decided to produce the annotations programmatically and restrict manual work to corrections.


But other types of contradictions can only be detected with the help of a body of background knowledge K: In these cases, T and H alone are mutually consistent.

we conducted experiments to evaluate how well AUCONTRAIRE distinguishes genuine contradictions from false positives.


These parameters and the prior distributions are estimated empirically, based on a sample of the data set of relations described in Section 5.1.

1 We cannot assume that a CD system knows, in advance, all the functional relations that might appear in a corpus.


At first glance, it may seem that constructing a regex to extract such entities is fairly straightforward.

given an input regex R 0 over some alphabet Σ, any other regex R over Σ is a candidate for our learning algorithm only if L(R) ⊆ L(R 0 ).


Since these other datasets may be from a different domain, we next performed a cross-domain test (i.e., training and testing on different domains).

in cross-domain performance (Table 1) ReLIE is significantly better than CRF (by 0.41 on average) .


Our new method, being essentially Bayesian inference, is potentially extensible to many other situations-other tasks, classifier architectures, and more complex features.

a log-linear model shows large and significant improvements, at all training sizes, when we incorporate rationales into its training via equation (4).


Other such θ-features were just innocent bystanders.

similarly, the language model can help explain why some words, though important, might not have been included in any rationale of r. If there are multiple annotators, one can learn different φ parameters for each annotator, reflecting their different annotation styles.


We present a generative model of how a given annotator, knowing the true θ, stochastically chooses rationales.

φ models these phrases' shape, via weights for several "language model" features.


It is also more principled, and could be adapted to help learn other kinds of probabilistic classifiers for quite different tasks.

let V = {v 1 , ..., v 17744 } be the set of word types with count ≥ 4 in the full 2000-document corpus.


At a preprocessing step, we binarize document vectors and remove low frequent words (both in terms  of p(w) and q(w)).

our estimate of |G\R| is twice the number of words with ρ(w) < 1, and then the number of topical words can be estimated as Instead of sharply thresholding topical and nontopical words, we can have them all, weighted with a probability of being topical.


The figure is not exactly compatible with ours, however, since that system used a different head extraction mechanism.

the latter is difficult since no structure is available inside segments, and we had to resort to computing upper-bound results using gold-standard input; despite this, the dependencybased system clearly outperformed the upper bound of the performance of the segment-based system.


A conjunctive query is specified as a Datalog rule.

to see why, we re-examine the proof: the large factor comes from assuming that all of R's first arguments which meet the PF definition are associated with exactly K min distinct second arguments.


HOLMES's representation is much simpler-Markov Logic Horn Clauses for inference rules coupled with a massive database of ground assertions.

this paper makes three main contributions: 1.


This is efficiently done using the inference rules to chain backwards from the query.

there are numerous web pages stating that "kale is high in calcium" and others declaring that "calcium helps prevent osteoporosis".


These results indicate that the majority of relations TEXTRUNNER extracted from text are APF, and so we can expect HOLMES's techniques will allow efficient inference over most relations.

it holds in our experience, and if necessary could be enforced by terminating the search for proof trees at a certain depth, e.g., log(m).


In future, we will explore more sophisticated features for the MERS model.

in order to know how the MERS models improve translation quality, we inspect the 1-best outputs of Lynx and Lynx+MERS.


this paper is organized as follows: Section 2 reviews the tAt model; Section 3 introduces the MERS model and describes feature definitions; Section 4 demonstrates a method to incorporate the MERS model into the translation model; Section 5 reports and analyzes experimental results; Section 6 gives conclusions.


These features are defined on source words.

tAt can be learned from word-aligned, sourceparsed parallel corpus.


Moreover, although TER-based alignment allows phrase shifts to accommodate the non-monotonic word ordering, all non-monotonic shifts are penalized equally no matter how short or how long the move is, and this penalty is set to be the same as that for substitution, deletion, and insertion edits.

the two main hypothesis alignment methods for system combination in the previous literature are GIZA++ and tER-based methods.


A single coarse pass with a 4-bit language model cuts decoding time almost in half.

the English language model is trained on the entire corpus of English parliamentary proceedings provided with the Europarl distribution.


This paper focuses on the above two special characteristics of the sentiment classification problem in the sentence level.

from it, we can obtain the similar conclusion, i.e.


The reason lies in that: the reviewers often follow some writing patterns, for example some reviewers prefer to concede an opposite factor before expressing his/her real sentiment.

for identifying the subjective sentences, there are several research, like (Wiebe et al, 2005).


As mentioned above, it's important for a classifier to consider the redundancy between different labels.

we conduct the experiments of polarity classification and sentimental strength rating on the corpus which will be introduced in section 5 later.


When a sentence including the word "good" is labeled as "PP", our model not only generate the state feature (good, "PP"), but also two implicit redundant state feature (good, "positive") and (good, "subjective").

the subsequent classification cannot provide constraint and correction to the results of the preceding step, which will lead to the accumulation and propagation of the classification errors.


In that work, we found that the projection of annotations across parallel texts can be successfully used to build a corpus annotated for subjectivity in the target language.

note that in this experiment the annotation of subjectivity is carried out on the original source language text, and thus expected to be more accurate than if it were applied on automatically translated text.


We translate raw text that is available in the target language into the source language, and then use a subjectivity annotation tool to label the automatically translated source language text.

we attempt to leverage on the resources available for English and, by employing machine translation, generate resources for subjectivity analysis in other languages.


One iteration of standard BP simply updates all the messages as in equations (3)-(4): one message per edge of the factor graph.

the training and test data were English, and for this section we filtered out sentences with non-projective links.


The parser should determine the values of the individual variables.

table 3 shows that global constraints are indeed important, and that it is essential to use tREE during training.


It is equivalent to forward-backward on an HMM with n observations (the L ij ) and n states per observation (the C j ), with a deterministic (thus sparse) transition function.

we would like to marginalize equation 1to obtain the distribution p(L 34 ) over L 34 = true vs. false, the distribution p(T 4 ) over tags, etc.


We give a new theoretical motivation for stacking parsers, in terms of extending a parsing model's feature space.

in the second category are those that employ exhaustive inference algorithms, usually by making strong independence assumptions, as is the case for edge-factored models (Paskin, 2001;McDonald et al., 2005a;McDonald et al., 2005b).


tion A of Malt + MST 2O (see Table 4), the difference being statistically insignificant (p > 0.05).

we appeal to the Matrix Tree Theorem for multi-digraphs to design polynomial-time algorithms for calculating both the partition function and edge expectations over all possible dependency graphs for a given sentence.


For example in Figure 2 there is a CC X : No matter what binarization we use, this CC will be recognized eventually.

as mentioned earlier, ICs are auxiliary means to generate CCs.


in summary, binarizations affect the efficiency of parsing primarily by affecting the number of iCs generated, where more shared and fewer failed iCs will help lead to higher efficiency.


We argue that binarizations affect parsing efficiency primarily by affecting the number of incomplete constituents generated, and the effectiveness of binarization also depends on the nature of the input (Section 4).

we argue that binarizations affect parsing efficiency primarily by affecting the number of incomplete constituents generated, and the effectiveness of binarization also depends on the nature of the input (Section 4).


we assume that the running time of CKY is primarily determined by the number of constituents generated in parsing.


In Section 7.3 we will use the iterative CKY for testing.

we propose a novel binarization method utilizing rich information learnt from training corpus (Section 5).


nelken & Schieber (2006) demonstrate the efficacy of a sentence-based tf*idf score when applied to comparable corpora.


1 One of the selected articles was missing from the Penn Treebank.

this is evidence for the fact that there is a complex interplay between readability factors: the entity grid factors which individually have very weak correlation with readability combine well, while adding the three additional discourse features to the likelihood of discourses relations actually worsens performance slightly.


This is achieved by parsing the English side of a parallel corpus and altering the phrase extraction algorithm to extract phrase labels alongside bilingual phrase pairs.

the labels shown in the figure are a fraction of those that can be derived for the phrase in the parallel corpus.


Many NLP tasks, such as SMT, which could benefit from paraphrases require broad coverage and may need to paraphrases for phrases which are not syntactic constituents.

the number of phrases for which we were able  Figure 2: In addition to extracting phrases that are dominated by a node in the parse tree, we also generate labels for non-syntactic constituents.


The former case, with trees on both sides, is often called tree-to-tree models; while the latter case, with trees on either source or target side, include both treeto-string and string-to-tree models (see Table 1).

the current dominant practice only uses 1-best trees, which adversely affects the rule set quality due to parsing errors.


However, a forest is an implicit collection of many more trees, each of which, when enumerated, has its own probability accumulated from of the parse hyperedges involved.

this is also 0.5 points better than (and twice as fast as) extracting on 30-best parses.


's (2007); the results are shown in Table 2.

we don't try to accumulate translations for the entire dataset, but simply maintain an exponentially-weighted moving average of previous translations.


In a finer-grained grammatical model, number/gender agreement would be enforced between such a pronoun and the NP in the posterior, so that the probability mass for these parses would be concentrated on cases where the pronoun is it.

scaling the model up for use with treebank-size grammars is another area for technical improvement.


This intersection thus represents the unnormalized posterior P C (T, w|w * ).

nearly all contemporary models of sentence comprehension assume clean input-that is, that the input to the sentence-level comprehension mechanism is a perfectly-formed, completely certain sequence of input tokens (words).


This information can be useful when designing a coreference system in an application where recall is more important than precision, or vice versa.

less attention has been given to the importance of selecting strong features to support learning a coreference model.


The task like query-based summarization that requires the use of more complex syntactic and semantics, the approaches with only BOW are often inadequate to perform fine-level textual analysis.

note that, for a word there can be more than one cluster.


When many of the competing sentences are included in the summary, the issue of information overlap between parts of the output comes up, and a mechanism for addressing redundancy is needed.

we conclude and give future directions in Section 7.


We initialized the sampler with a configuration derived from the word alignments generated by the baseline.

first, they must choose among all possible configurations of the unfrozen local state.


Previous phrase alignment work has primarily mitigated this tendency by constraining the inference procedure, for example with word alignments and linguistic features (Birch et al., 2006), or by disallowing large phrase pairs using a noncompositional constraint (Cherry and Lin, 2007;Zhang et al., 2008).

similarly, we can parameterize the Dirichlet process with a concentration parameter α (that affects only the variance) and a base distribution M 0 that determines the mean of the samples.


We describe the first tractable Gibbs sampling procedure for estimating phrase pair frequencies under a probabilistic model of phrase alignment.

a generative model that explicitly aligns pairs of phrases e, f gives us well-founded alternatives for estimating phrase pair scores.


Previous semantic role classifiers always did the classification problem in one-step.

there are two kinds of argument sequences in Jiang et al.


This can be explained by the fact that both in the development and test set, the amount of core arguments outperforms that of adjuncts.

we have different opinions of the traditional architecture.


This is not surprising, as the α and α specify how likely the samplers are to consider novel tags, and therefore directly influence the sampler's mobility.

the previous section described six different unsupervised estimators for HMMs.


Training on these huge databases will only be possible with an appropriate selection of promising triplets.

the effect is often shown offline in reranking experiments using n-best lists.


It can be observed that the Arabic name's translations include several English words with similar spellings, all of which are correct translations.

since the evaluation test set is not publicly available, we have split the publicly available training corpus into an 85%/15% data split.


In most NLP problems, the data is present in structured forms, like strings or trees, and this structural information can be effectively passed to a kernel-based learning algorithm using an appropriate kernel, like a string kernel (Lodhi et al., 2002) or a tree kernel (Collins and Duffy, 2001).

we believe this is because of the shortcomings of the syntactic tree kernel pointed out in Subsection 2.2.


Note that the IBM Model 1 solely relies on word co-occurrence statistics obtained from parallel strings in order to learn translation probabilities.

obviously, we need to build a translation model in advance.


As an alternative term weighting approach, we have used a variant of Mihalcea and Tarau (2004)'s Tex-tRank, a graph-based ranking model for keyword extraction which achieves state-of-the-art accuracy without the need of deep linguistic knowledge or domain-specific corpora.

we make these observations: • The translation models learned from the (Q Q) corpus have less vocabularies but more average translations per word than the ones learned from the (Q A) corpus.


The similarity is calculated as the reverse of the harmonic mean of ranks as sim(a i , a j ) = 1 2 ( 1 r j + 1 r i ), where r j and r i refer to the rank of the a j and a i when a i and a j are given as queries, respectively.

prior work on translation-based Q&A retrieval does not recognize this issue and uses the translation model as it is; essentially no attention seems to have been paid to improving the performance of the translation-based approach by enhancing the quality of translation models.


As an alternative, we used an SVM with a quadratic kernel, since it can also capture such pairwise interactions of features.

we consider each labeled feature as a propositional variable, where true or false corresponds to whether the label of the feature is positive or negative respectively.


In this example, although 'deny' is rightly assigned a positive weight and 'moot' is rightly assigned a negative weight, when both features co-occur in a docket entry (as in 'deny as moot'), it makes the label negative.

clearly, the within-sentence conjunctive behavior of the labeled features is captured by applying logical conjunctions to the labeled features within a sentence.


The L 1 -regularized logistic regression was comparable to the SVM with linear kernel in this experiment.

the method presented in this paper allows only one region of change in string transformation.


Also, frames are based on more complex information than word senses, so that their manual development is much more demanding Subirats and Petruck, 2003).

the current FrameNet release contains 795 frames and about 10,000 LUs.


(1) Finding them is really a pain, you know (2) The company's decided that teletext is outdated Subjective questions are questions in which the speaker is eliciting the private state of someone else.

if a dialogue act overlaps with an annotation of a particular type, then the segment is considered to be labelled with that type.


In text, there has been a significant amount of research on subjectivity and sentiment recognition, ranging from work at the phrase level to work on classifying sentences and documents.

there has been a wealth of research over the past several years on automatic subjectivity and sentiment analysis in text, including on-line media.


In the sentence What year did the Vietnam War end?, the newspaper-trained POS tagger incorrectly assigns the POS tag NN (common noun) to the verb end, since verb-final sentences are atypical for the PTB.

of course, it would be useful to have a way of predicting which level of annotation would be most effective for adapting to a new domain before the annotation begins.


The explicit representation of predicate coordination also provides deeper relations.

interested readers could refer to (Shen, 2006) for detailed description of the learning algorithms for level-1 dependency.


In our approach, each fragment in the graph is associated with a hidden structure, which means that we cannot reduce it to a labelling task.

furthermore, we use averaged weights (Collins, 2002;freund and Schapire, 1999) in Algorithm 1.


In Section 3, we will present a Perceptron like algorithm (Collins, 2002;Daumé III and Marcu, 2005) to obtain the parameters.

we will train and evaluate an LTAG dependency parser over the LTAG treebank described in Shen et al.


It might be hard for the other baseline models to identify topics accurately.

furthermore, the topics having the largest posterior probabilities for the words are also shown.


Variational inference is modified appropriately.

in HTM with propagation of topic distributions from cited documents, the words of a document can be more accurately assigned into topics.


The topics are further characterized as distributions of words, as in the conventional topic models.

if the content of a document is diverse, then words in the document are likely to be assigned into wrong topics by the existing approaches.


This step will be executed when the target case role is syntactically necessary and determined by the co-occurrence of the case roles and predicate or event noun without syntactic clues, such as dependency, functional words and voices.

the overall results are shown in table 7.


The VPP category type, which tries to extract prepositional objects, contains  the most number of 'k' annotations; this is because players often put answers that are subjects or objects of the verb, such as "pizza" for "Things that are eaten with".

to increase data quality, in Categodzilla for each category there are three boxes.


1 Each document was divided into two parts comprising its executive summary, and the remainder, referred to here as the source.

finally, since the WCM model only serves to select words from the auxiliary sentences, words from the key sentence must be given scores as well.


We do this by examining sample summary sentences for conventional juxtapositions of concepts.

a preprocessing stage extracted text from the PDF files and segmented the documents into executive summary and source sections.


In our work, this general proposition is equivalent to the core information for the summary sentence before the incorporation of supplementary material.

we propose and evaluate a method called "Seed and Grow" for selecting such auxiliary information.


In order to assess the performance of the proposed approach, we collected 1000 product reviews from a popular Chinese IT product web site-IT168 10 .

the resources for other languages are limited.


(2007) do not handle directly; they are often implicitly taken care of when reordering DNPs instead.

3, which is similar to the Hiero rules: (3) one of the [X, 0] > .


As expected, RD alone does not match combined precision and recall of state-of-the-art supervised systems.

affinity propagation (aP) is the most efficient approximation for the p-median problem that we are aware of, which also has the property of not requiring the number of clusters as an explicit input (Frey and Dueck, 2007).


Order matters in that S 1 ..S 2 is considered a different entity co-occurrence (and therefore potential relation) than S 2 ..S 1 .

there can be many other such indicators, many discourse or corpus specific.


Our work can be seen as a generalization of theirs to relationships of any kind, and we extend the use of frequency patterns to finding general n-gram entities rather than single word entities.

this step is still necessary for bootstrapping RE, since machine learning approaches require a sufficient number of positive examples to train the extractor.


We will call this model CIP (Context Independent Propagation).

4 Given this observation, we are interested in algorithms that can easily integrate and make effective use of diverse input types.


The brevity penalty can also be seen as a standin for recall.

we did not attempt max-4-GRR training, but we evaluated the word-deletion test sets obtained by max-B and max-B- training using 4-GRR.


It would be difficult indeed to argue that an evaluation metric, in order to be fair, must be decomposable into subsentential scores, and we make no such claim.

let g k (w) be the multiset of all k-grams of a sentence w. We are given a sequence of candidate translations c to be scored against a set of sequences of reference translations, {r j } = r 1 , .


We hope that our approach will provide some insight into the design of lattice-based search procedures along with the use of non-linear, global loss functions such as BLEU.

to achieve this, we make use of the properties of n-gram matches.


If the number of hypotheses is greater than, say 20,000, the N -best MBR becomes computationally expensive.

to a phrase-based SMT system, a syntax based SMT system (e.g.


In our method, at every EM iteration, during the Maximization step, we set such cases back to uniform.

as it has been found out by (DeNero et al., 2006), it is not easy to come up with a simple, effective prior distribution over segmentations that allows for improved phrase pair estimates.


Up to the prior over segmentations (see next), we implement the above model using a weighted version of the binary SCFG as follows: • The weight for lexical rules is given by P (XP → f, e) := P (f | e), where f, e is a phrase-pair.

zero distributions: When a phrase e does not occur in H i , all its pairs f, e in π i will have zero counts.


This way, phrase pair extraction goes handin-hand with estimating the probabilities.

hence, the number of binarizations that a segmentation has under the bSCFG is a direct function of the ways in which the containers combine among themselves (monotone vs. inverted/crossing) within segmentations, and provides a more accurate measure of container productivity than container length.


this holds for the SCFG implementing Inversion Transduction Grammar (Wu, 1997).


Hence, we need a different model for generating pronouns.

given a document D, the number of coreference clusterings is exponential in the number of mentions in D, even if we limit our attention to those that are valid.


H i is associated with the ith level of the Bell tree, and is used to store the most probable ith-order partial partitions.

so, following H&K, we keep soft counts for each of these properties and use them rather than perform hard sampling.


Our second baseline is obtained by running only one iteration of our EM-based coreference model.

head generation in the basic model is too simplistic: it has a strong tendency to assign the same entity index to mentions having the same head.


Before introducing this pronoun head model, we need to augment the set of entity-specific parameters, which currently contains only a distribution over heads (φ h Z ).

m 7 ij | C ij ) into a product of three probabilities: , and P (m 7 ij | C ij ).


CEAF precision and recall are both positively correlated with the score of this optimal alignment, which is computed by summing over each aligned entity pair the number of mentions that appear in both entities of that pair.

classifying mention pairs using such iterative approaches is undesirable for coreference resolution: since the non-coreferent mention pairs significantly outnumber their coreferent counterparts, the resulting classifiers generally have an increasing tendency to (mis)label a pair as non-coreferent as bootstrapping progresses (see Ng and Cardie (2003)).


In this paper, we present the first unsupervised approach that is competitive with supervised ones.

unsupervised coreference resolution is much more difficult.


The independence assumption from the first approximation can be relaxed without increasing the asymptotic time complexity by considering as independent only chunks of contiguous POS tags that are at least a certain number of tokens apart.

equations (1) and (2) below show the traditional method for computing the optimal outputŷ in a pipeline model, assuming a linear scoring function defined by w and φ.


This is related to joint inference (Daumé III et al., 2006).

intuitively, no: a function χ that returns 1 regardless of its inputs is clearly not useful.


NER2 seems wrong because there is an NNP 1 (proper noun) that is not part of a named entity (word 5).

can we learn f 2 (and/or f 1 ) using significantly fewer labeled examples than if we did not have χ?


The key difference is that in co-training, one assumes that the two "views" are on the inputs; here, we can think of the two output spaces as being the difference "views" and the compatibility function χ being a method for reconciling these two views.

if C 2 is learnable from large amounts of labeled data, then it is also learnable from small amounts of labeled data and large amounts of f 1 -labeled data.


A common technique for learning such experts in the Weighted Majority algorithm (Littlestone and Warmuth, 1994), which weighs a mixture of experts (classifiers).

in this framework, all tasks are close to some underlying mean w s and each one deviates from this mean by w d .


A multi-task approach can be adapted to our setting by using our classifier combination techniques.

feature similarity -possibly measured using unlabeled data -could be used to weigh source domains.


Timebank Annotation of wsj 0551 The large amount of unlabeled relations in the corpus presents several problems.

equation 2 (the first constraint) simply says that each variable must be 0 or 1.


(2008) shows that average internet users can make before/after decisions with very high confidence, although the distinction with an unknown relation is not as clear.

the first chooses before for all three relations, while the second chooses after.


13' should thus be tagged as being before this expression.

the ILP approach does not alone improve performance on the event-event tagging task, but adding the time expression relations greatly increases the global constraint results.


These features are the highest performing set on the basic 6-way classification of Timebank.

we also add new time-time links that are deduced from the logical time intervals that they describe.


Boundedness has many surface manifestations that can be directly observed, and one of them is whether the verb is stative or dynamic.

another key decision we made when formulating our annotation scheme is to define an abstract "tense" that do not necessarily model the actual tense system in any particular language that has grammatical tense.


As a motivating example, let us return to the English/Spanish pair "file name" and "nombre de archivo".

early experiments showed that this feature alone was not enough to allow even common insertions, since the probability of even the most common insertion templates is much lower than that of unlexicalized templates.


You can see that the size of the Finnish vocabulary is about six times larger (510,632 words) than the English vocabulary size (88,880 words).

for each pair of lists the cognacy of a form can be positive, negative or indeterminate.


The morphological complexity of the language pairs involved in translation is widely recognized as one of the factors influencing translation performance.

the first question we are interested in answering is which factors contribute most and how they interact.


The second set of results excludes Finnish in the source and target language.

words are often polymorphemic, and become remarkably long.


The intuition behind weighted f-score is that DAs that are linked multiple times by multiple annotators are the most informative.

rambow carried out summarization work on a different, unavailable email corpus, and so we re-implemented their summarization system for our current email data.


Also, SCFGs use a finite set of context-free rewrite rules to define the model, where the rules are possibly weighted.


Given a semantic category M a , we first pick an MR production m a that has the form , which gives us the function symbol p α as well as the child semantic categories M b and M c .

it is shown to be less effective on ROBOCUP corpus.


also manually collected a handful of content-word negators (referred as "general polarity shifters"), but not extensively.

our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning-based methods that do not incorporate compositional semantics (accuracy of 89.7% vs. 89.1%), but (2) a method that integrates compositional semantics into learning performs better than all other alternatives (90.7%).


Once we determine the intermediate decision variables, we apply the heuristic rules motivated by compositional semantics (from Table 2) in order to obtain the final polarity y of x.

we add this feature for each x i that is not a stop word.


With the MANLI system, we have demonstrated how to overcome this lack of data by utilizing external lexical resources, and how to gain additional power from a phrase-based representation of alignment.


Beginning from an arbitrary alignment, we make a series of local steps, at each iteration sampling from a set of possible successors according to scores assigned by our scoring function.

since punctuation matters little in inference, such recall errors probably should be forgiven.


We note that these predicted unicity distances are a bit too rosy, according to our empirical message equivocation curves.

we also empirically explore the concepts in Shannon's (1949) paper on information theory as applied to cipher systems.


Given a group of WFSTs, each of which models a stage of a mapping cascade, the composition operation provides an efficient approach to combine them into a single one (Mohri et al., 2002;Mohri et al., 1996).

in (Takaaki Hori and Minami, 2003), the WFST has been further used for language model adaptation, where language models of different vocabularies that represented different styles were integrated through the framework of speech translation.


Moreover, a SuperARV language model was presented (Wang and Harper, 2002), in which lexical features and syntactic constraints were tightly integrated into a linguistic structure of SuperARV serving as a class in the model.

through the reranking paradigm, a new best sentence hypothesis is obtained.


In practice, the part-of-speech (POS) information, capturing the syntactic role of words, has been widely used in clustering words (Wang and Vergyri, 2006;Maltese et al., 2001;Samuelsson and Reichl, 1999).

both Chinese characters and words are considered as model units in this study, and a word re-segmentation process on recognition hypotheses is necessary, where an n-gram language model based on word classes is adopted.


The features that achieve the lowest combined cross-entropy and WER on the development set are starred.

with similar investments in acoustic modeling and other areas of natural language processing, we look forward to an ever increasing diversity of practical speech and natural language applications.


To compute the word error rate, we use a speakerindependent speech recognizer (Glass, 2003) with a large-margin discriminative acoustic model (Chang, 2008).

tuning the weighting and interpolation parameters jointly further allows the estimation of the n-gram probabilities to utilize the best matching LM components.


To address the mismatch, they partition the training documents by their metadata attributes and compute a measure of the likelihood that an n-gram will appear in a new partitioned segment.

nevertheless, n-gram weighting consistently reduces the absolute test set WER by a statistically significant average of 0.3%, according to the Matched Pairs Sentence Segment Word Error test (Pallet et al., 1990).


Many application domains in machine learning suffer from a dearth of matched training data.

one way to estimate the specificity of an n-gram across partitions is to measure the n-gram frequency f , or the fraction of partitions containing an n-gram.


Complementary to our work, this technique also utilizes segmentation and metadata information.

for future work, we plan to investigate the use of the initial recognition hypotheses as the development set, as well as manually transcribing a subset of the test set utterances.


The main problem with this approach is that often the local optimum is suboptimal on the global scale, for example if a word occurs on a later position again.

but as this diverts from our goal to optimize the sentence-level scores, we have not implemented it so far.


Doing so creates long-range dependencies, which oppose local decisions.

create a unique node for each "running" word in R -e.g.


The CLIR problem can be framed probabilistically as: Given a query Q, find a document D that maximizes the equation Pr(D is rel|Q).

in sections 3.1 and 3.2, we describe the model adaptation for the language model and translation model, respectively.


2 The Pr(Q) is constant across all documents.

in addition, while newswire is an excellent source of comparable text, it is unclear how well this method can be used on newsgroups or spoken data, where the fluency of the source text is diminished.


Multi-scale grammars, in contrast, give higher accuracies using smaller grammars than previous work in this direction, outperforming top generative models in grammar size and in parsing accuracy.

learning a grammar for parsing requires the estimation of a more highly articulated model than the naive CFG embodied by such treebanks.


Table 1 shows how many sentences were included in each dataset.

let c(n, ) select the children of n with label .


Then, we simplify (2) by fixing the alignments a 0 : This optimization has no latent variables and is therefore convex and straightforward.

first, as before, we only include the sentence pairs from our bilingual corpus to fully demonstrate the gains made by joint parsing.


The other recipe that is currently used on a large scale is to measure the performance of a parser on existing treebanks, such as WSJ (Marcus et al., 1993), and assume that the accuracy measure will carry over to the domains of interest.

these F-score figures are shown in the third column of table 6.


Since the SELPREF-POW model performed best in our evaluation, we read this as evidence that potentiation helps to suppress noise introduced by mis-identified subject and object fillers.


Current kernels are mostly tree kernels that compare syntactic structure, and use semantic information mostly for smoothing syntactic similarity (Moschitti and Quarteroni, 2008).

our next step will be to integrate information from multiple relations (such as both the subject and object positions of a verb) into the computation of context-specific meaning.


Random-walk similarity has also been used for lexical smoothing for prepositional word attachment (Toutanova et al., 2004) and query expansion (Collins-Thompson and Callan, 2005).

the graph shown in the figure includes the dependency analysis of two sentences: "boys like playing with all kinds of cars", and "girls like playing with dolls".


In order to facilitate the comparison with previously reported results, the question classification performance is measured by accuracy, i.e., the proportion of the correctly classified questions among all test questions.

this research was supported by British telecom grant Ct1080028046 and BISC Program of UC Berkeley.


One possible reason is that approximately 40% of the best answers were chosen by the community and not the asker herself, are hence not necessarily representative of the asker intent.


Although much progress has been made, answering complex, opinion, and even many factual questions automatically is still beyond the current state-of-the-art.

we focus on the following research questions: • Can we utilize the inherent structure of the CQA interactions and use the unlimited amounts of unlabeled data to improve classification performance, and/or reduce the amount of manual labeling required?


(Chapelle et al., 2006) is a natural approach for this setting.

we will only consider questions and best answers in the following experiments with GE and CoCQA.


In future experiments, we will investigate its performance on question answering tasks in languages such as Chinese and Japanese.

table 7 shows one of many examples where SEAL was penalized for finding additional correct answers.


Then the annotation experiment which was used to verify that the form-based representation can be understood and applied by other human annotators is discussed in Section 3.2.

this is contrasted with a dialog structure recognition process (Alexandersson and Reithinger, 1997;Bangalore et al., 2006;Hardy et al., 2004), where pre-specified dialog structure components are recognized as a dialog progresses.


We propose a novel approach that determines a list of stop words directly from word distribution in each data set.

predicting the boundaries of fine-grained subtasks is more difficult even with a supervised learning approach (Arguello and Rosé, 2006).


Of course, as the analysis window slides in time, it is necessary to keep track of the temporal event FIFO list to make sure that the events at the top are removed as soon as they fall out of the analysis window.

using the set of dialogs we constructed different query vectors and affected these queries using various noise conditions, and then we utilized these vectors to perform a simple document query classification experiment.


The most common include direct quotation, emphasis, clarification, parenthetical comments, tags, and trigger switches.

at the expense of making this task even more difficult, we decided against trying to include this type of information and include only lexical and syntactic features, to evaluate a practical and cost effective method for this task.


Bold face indicates a better performance.


Web and News indicate the sentence length in the Web and a newspaper corpora, respectively.

some phrases have two or more idiomatic meanings without context.


In the rest of this paper, we perform domain adaptation experiments for WSD, focusing on domain adaptation methods that use in-domain annotated data.

using the WSD examples provided through OntoNotes, we conduct the first large-scale WSD evaluation involving hundreds of word types and tens of thousands of sense-tagged examples, while adopting a coarse-grained sense inventory.


In SENSEVAL-2, the best performing system (Mihalcea and Moldovan, 2001) in the English all-words task achieved an accuracy of 69.0%, while in SENSEVAL-3, the best performing system (Decadt et al., 2004) achieved an accuracy of 65.2%.

besides providing WSD examples that were sense-tagged with a high ITA, the project also addressed the previously discussed issues of a lack of training and test data.


Given these definitions, we see that the (M T M ) n term of smaller n corresponds to lower-order cooccurrence, which is accurate but sparse, and the (M T M ) n term of larger n corresponds to higherorder co-occurrence, which is dense but possibly giving too much weight on unrelated instances extracted by generic patterns.

the proposed methods outperform by a large margin the most frequent sense baseline and both Simplified-and Filtered Espresso.


Previous work with queries (Allan and Raghavan, 2002) considered that short queries may be ambiguous in their part of speech and that different documents are relevant depending on how this ambiguity is resolved.

this system does not improve significantly over the tagger trained on the lower-cased corpus.


Since search queries are part of an information retrieval task, we would like to demonstrate that partof-speech tagging can assist with that task.

the WSJ-trained tagger labeled 2293 of 4759 (48.2%) tags correctly, a number well below the baseline performance, demonstrating that application of the contextual rules that Brill learns from the syntax of natural-language corpora has a negative effect on accuracy in the context of queries.


Essentially, every sentence contains at least one verb.

similarly, (strzalkowski et al., 1998) added a layer of natural language processing using part-of-speech tags and syntactical parsing to the common statistical information-retrieval framework, much like experiments detailed in sections 4 and 5.


The data used to train the tagger therefore has a significant effect on its accuracy (Table 3).

the corpora the Brown tag set was designed for consists primarily of complete, naturallanguage sentences.


is the majority of all text appearing on the Internet.

we would like to thank Yi Su, Sanjeev Khudanpur, and the anonymous reviewers for their helpful comments.


This gives us a reliable way to seed and bootstrap a list of informal phrases as will be discussed in Section 4.1.

we test our system on manually collected test examples 2 , and find that the (formal-informal) relationship discovery and extraction process using our method achieves an average precision of more than 60%.